{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "H2o_classification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUFYp6K3s9Uf",
        "outputId": "a63a4815-5ad4-46e7-8229-ea5a90b7e738"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting h2o\n",
            "  Downloading h2o-3.36.1.1.tar.gz (177.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 177.0 MB 20 kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from h2o) (2.23.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from h2o) (0.8.9)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from h2o) (0.16.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (3.0.4)\n",
            "Building wheels for collected packages: h2o\n",
            "  Building wheel for h2o (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for h2o: filename=h2o-3.36.1.1-py2.py3-none-any.whl size=177068062 sha256=fbcb3aea958c8f30da51da1806ebd5decdf7804b9e422826ba9619af5649a6d0\n",
            "  Stored in directory: /root/.cache/pip/wheels/a6/d9/ab/5442447c7e2ccf07f66aa8b79f3877ce5382f0b95e6c0c797b\n",
            "Successfully built h2o\n",
            "Installing collected packages: h2o\n",
            "Successfully installed h2o-3.36.1.1\n"
          ]
        }
      ],
      "source": [
        "!pip install h2o"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_circles\n",
        "import pandas as pd\n",
        "X, y = make_circles(n_samples=1000, noise=0.2, factor=0.5, random_state=9)\n",
        "df = pd.DataFrame(X, columns=['x1','x2'])\n",
        "df['y'] = y\n",
        "df.head()\n",
        "df.to_csv('circle.csv', index=False, header=True)"
      ],
      "metadata": {
        "id": "BJPMA_N9t140"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import h2o\n",
        "h2o.init()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "ku-yTTWKamT7",
        "outputId": "07acdd08-927e-4c88-fc75-893b30fe4d56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.15\" 2022-04-19; OpenJDK Runtime Environment (build 11.0.15+10-Ubuntu-0ubuntu0.18.04.1); OpenJDK 64-Bit Server VM (build 11.0.15+10-Ubuntu-0ubuntu0.18.04.1, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.7/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmpm2fsae68\n",
            "  JVM stdout: /tmp/tmpm2fsae68/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmpm2fsae68/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "--------------------------  ----------------------------------\n",
              "H2O_cluster_uptime:         05 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.36.1.1\n",
              "H2O_cluster_version_age:    27 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_45enk6\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    3.172 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         locked, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "Python_version:             3.7.13 final\n",
              "--------------------------  ----------------------------------"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>05 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.36.1.1</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>27 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_45enk6</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>3.172 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>locked, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.7.13 final</td></tr></table></div>"
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "class_df = h2o.import_file(\"circle.csv\",\\\n",
        "                    destination_frame=\"circle_df\")\n",
        "class_df['y'] = class_df['y'].asfactor()\n",
        "\n",
        "train_df,valid_df,test_df = class_df.split_frame(ratios=[0.6, 0.2],\\\n",
        "                   seed=133)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qV-PqH_SaTbv",
        "outputId": "e77293d0-794b-44ab-faba-14f9f227db55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from h2o.automl import H2OAutoML as AutoML\n",
        "aml = AutoML(max_models = 10, max_runtime_secs=100, seed=2)\n",
        "aml.train(training_frame= train_df, \\\n",
        "        validation_frame=valid_df, \\\n",
        "        y = 'y', x=['x1','x2'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WsGKnHpVauoL",
        "outputId": "0ea33499-5743-4923-abf8-b9e15f1a0992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AutoML progress: |\n",
            "06:13:56.745: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.\n",
            "\n",
            "███████████████████████████████████████████████████████████████| (done) 100%\n",
            "Model Details\n",
            "=============\n",
            "H2OStackedEnsembleEstimator :  Stacked Ensemble\n",
            "Model Key:  StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356\n",
            "\n",
            "No model summary for this model\n",
            "\n",
            "ModelMetricsBinomialGLM: stackedensemble\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 0.05639448262225793\n",
            "RMSE: 0.23747522528099205\n",
            "LogLoss: 0.1911950519379175\n",
            "Null degrees of freedom: 606\n",
            "Residual degrees of freedom: 602\n",
            "Null deviance: 841.4790297525691\n",
            "Residual deviance: 232.1107930526319\n",
            "AIC: 242.1107930526319\n",
            "AUC: 0.9816744832377975\n",
            "AUCPR: 0.9826583329344181\n",
            "Gini: 0.963348966475595\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.47149357032172445: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              0      1   Error           Rate\n",
              "0      0  283.0   21.0  0.0691   (21.0/304.0)\n",
              "1      1   23.0  280.0  0.0759   (23.0/303.0)\n",
              "2  Total  306.0  301.0  0.0725   (44.0/607.0)"
            ],
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0691</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>23.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>0.0759</td>\n",
              "      <td>(23.0/303.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>306.0</td>\n",
              "      <td>301.0</td>\n",
              "      <td>0.0725</td>\n",
              "      <td>(44.0/607.0)</td>\n",
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          "text": [
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "                         metric  threshold       value    idx\n",
              "0                        max f1   0.471494    0.927152  188.0\n",
              "1                        max f2   0.311703    0.950387  225.0\n",
              "2                  max f0point5   0.726434    0.948738  141.0\n",
              "3                  max accuracy   0.471494    0.927512  188.0\n",
              "4                 max precision   0.998487    1.000000    0.0\n",
              "5                    max recall   0.115336    1.000000  283.0\n",
              "6               max specificity   0.998487    1.000000    0.0\n",
              "7              max absolute_mcc   0.471494    0.855041  188.0\n",
              "8    max min_per_class_accuracy   0.471494    0.924092  188.0\n",
              "9   max mean_per_class_accuracy   0.471494    0.927507  188.0\n",
              "10                      max tns   0.998487  304.000000    0.0\n",
              "11                      max fns   0.998487  302.000000    0.0\n",
              "12                      max fps   0.000469  304.000000  399.0\n",
              "13                      max tps   0.115336  303.000000  283.0\n",
              "14                      max tnr   0.998487    1.000000    0.0\n",
              "15                      max fnr   0.998487    0.996700    0.0\n",
              "16                      max fpr   0.000469    1.000000  399.0\n",
              "17                      max tpr   0.115336    1.000000  283.0"
            ],
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              "      <th>metric</th>\n",
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              "      <th>0</th>\n",
              "      <td>max f1</td>\n",
              "      <td>0.471494</td>\n",
              "      <td>0.927152</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>max f2</td>\n",
              "      <td>0.311703</td>\n",
              "      <td>0.950387</td>\n",
              "      <td>225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>max f0point5</td>\n",
              "      <td>0.726434</td>\n",
              "      <td>0.948738</td>\n",
              "      <td>141.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>max accuracy</td>\n",
              "      <td>0.471494</td>\n",
              "      <td>0.927512</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max precision</td>\n",
              "      <td>0.998487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>max recall</td>\n",
              "      <td>0.115336</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>283.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>max specificity</td>\n",
              "      <td>0.998487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max absolute_mcc</td>\n",
              "      <td>0.471494</td>\n",
              "      <td>0.855041</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>max min_per_class_accuracy</td>\n",
              "      <td>0.471494</td>\n",
              "      <td>0.924092</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max mean_per_class_accuracy</td>\n",
              "      <td>0.471494</td>\n",
              "      <td>0.927507</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max tns</td>\n",
              "      <td>0.998487</td>\n",
              "      <td>304.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>max fns</td>\n",
              "      <td>0.998487</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>max fps</td>\n",
              "      <td>0.000469</td>\n",
              "      <td>304.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>max tps</td>\n",
              "      <td>0.115336</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>283.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>max tnr</td>\n",
              "      <td>0.998487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>max fnr</td>\n",
              "      <td>0.998487</td>\n",
              "      <td>0.996700</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>max fpr</td>\n",
              "      <td>0.000469</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>max tpr</td>\n",
              "      <td>0.115336</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>283.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "\n",
            "Gains/Lift Table: Avg response rate: 49.92 %, avg score: 49.99 %\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group  cumulative_data_fraction  lower_threshold      lift  \\\n",
              "0       1                  0.011532         0.996075  2.003300   \n",
              "1       2                  0.021417         0.995416  2.003300   \n",
              "2       3                  0.031301         0.994614  2.003300   \n",
              "3       4                  0.041186         0.994245  2.003300   \n",
              "4       5                  0.051071         0.993189  2.003300   \n",
              "5       6                  0.100494         0.990628  2.003300   \n",
              "6       7                  0.149918         0.986728  2.003300   \n",
              "7       8                  0.200988         0.981443  2.003300   \n",
              "8       9                  0.299835         0.938521  1.969912   \n",
              "9      10                  0.400329         0.774994  1.970459   \n",
              "10     11                  0.500824         0.449722  1.280799   \n",
              "11     12                  0.599671         0.220562  0.634378   \n",
              "12     13                  0.700165         0.097272  0.131364   \n",
              "13     14                  0.799012         0.037899  0.000000   \n",
              "14     15                  0.899506         0.011835  0.000000   \n",
              "15     16                  1.000000         0.000469  0.000000   \n",
              "\n",
              "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
              "0          2.003300       1.000000  0.997295                  1.000000   \n",
              "1          2.003300       1.000000  0.995595                  1.000000   \n",
              "2          2.003300       1.000000  0.994880                  1.000000   \n",
              "3          2.003300       1.000000  0.994342                  1.000000   \n",
              "4          2.003300       1.000000  0.993565                  1.000000   \n",
              "5          2.003300       1.000000  0.991902                  1.000000   \n",
              "6          2.003300       1.000000  0.988659                  1.000000   \n",
              "7          2.003300       1.000000  0.984044                  1.000000   \n",
              "8          1.992293       0.983333  0.965531                  0.994505   \n",
              "9          1.986812       0.983607  0.869357                  0.991770   \n",
              "10         1.845145       0.639344  0.609772                  0.921053   \n",
              "11         1.645568       0.316667  0.332833                  0.821429   \n",
              "12         1.428235       0.065574  0.148837                  0.712941   \n",
              "13         1.251546       0.000000  0.060527                  0.624742   \n",
              "14         1.111722       0.000000  0.025715                  0.554945   \n",
              "15         1.000000       0.000000  0.004397                  0.499176   \n",
              "\n",
              "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
              "0           0.997295      0.023102                 0.023102  100.330033   \n",
              "1           0.996510      0.019802                 0.042904  100.330033   \n",
              "2           0.995995      0.019802                 0.062706  100.330033   \n",
              "3           0.995599      0.019802                 0.082508  100.330033   \n",
              "4           0.995205      0.019802                 0.102310  100.330033   \n",
              "5           0.993581      0.099010                 0.201320  100.330033   \n",
              "6           0.991958      0.099010                 0.300330  100.330033   \n",
              "7           0.989947      0.102310                 0.402640  100.330033   \n",
              "8           0.981898      0.194719                 0.597360   96.991199   \n",
              "9           0.953647      0.198020                 0.795380   97.045934   \n",
              "10          0.884646      0.128713                 0.924092   28.079857   \n",
              "11          0.793687      0.062706                 0.986799  -36.562156   \n",
              "12          0.701132      0.013201                 1.000000  -86.863604   \n",
              "13          0.621882      0.000000                 1.000000 -100.000000   \n",
              "14          0.555278      0.000000                 1.000000 -100.000000   \n",
              "15          0.499917      0.000000                 1.000000 -100.000000   \n",
              "\n",
              "    cumulative_gain  kolmogorov_smirnov  \n",
              "0        100.330033            0.023102  \n",
              "1        100.330033            0.042904  \n",
              "2        100.330033            0.062706  \n",
              "3        100.330033            0.082508  \n",
              "4        100.330033            0.102310  \n",
              "5        100.330033            0.201320  \n",
              "6        100.330033            0.300330  \n",
              "7        100.330033            0.402640  \n",
              "8         99.229319            0.594070  \n",
              "9         98.681226            0.788801  \n",
              "10        84.514504            0.845145  \n",
              "11        64.556813            0.772983  \n",
              "12        42.823529            0.598684  \n",
              "13        25.154639            0.401316  \n",
              "14        11.172161            0.200658  \n",
              "15         0.000000            0.000000  "
            ],
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              "\n",
              "  <div id=\"df-bb1cf892-a3b1-4754-a7bd-28580f9d0895\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>group</th>\n",
              "      <th>cumulative_data_fraction</th>\n",
              "      <th>lower_threshold</th>\n",
              "      <th>lift</th>\n",
              "      <th>cumulative_lift</th>\n",
              "      <th>response_rate</th>\n",
              "      <th>score</th>\n",
              "      <th>cumulative_response_rate</th>\n",
              "      <th>cumulative_score</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>cumulative_capture_rate</th>\n",
              "      <th>gain</th>\n",
              "      <th>cumulative_gain</th>\n",
              "      <th>kolmogorov_smirnov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.011532</td>\n",
              "      <td>0.996075</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997295</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997295</td>\n",
              "      <td>0.023102</td>\n",
              "      <td>0.023102</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.023102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.021417</td>\n",
              "      <td>0.995416</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995595</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996510</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.042904</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.042904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.031301</td>\n",
              "      <td>0.994614</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994880</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995995</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.062706</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.062706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.041186</td>\n",
              "      <td>0.994245</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994342</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995599</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.082508</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.082508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.051071</td>\n",
              "      <td>0.993189</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993565</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995205</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.102310</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.102310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.100494</td>\n",
              "      <td>0.990628</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991902</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993581</td>\n",
              "      <td>0.099010</td>\n",
              "      <td>0.201320</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.201320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.149918</td>\n",
              "      <td>0.986728</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.988659</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991958</td>\n",
              "      <td>0.099010</td>\n",
              "      <td>0.300330</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.300330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.200988</td>\n",
              "      <td>0.981443</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.984044</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.989947</td>\n",
              "      <td>0.102310</td>\n",
              "      <td>0.402640</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.402640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.299835</td>\n",
              "      <td>0.938521</td>\n",
              "      <td>1.969912</td>\n",
              "      <td>1.992293</td>\n",
              "      <td>0.983333</td>\n",
              "      <td>0.965531</td>\n",
              "      <td>0.994505</td>\n",
              "      <td>0.981898</td>\n",
              "      <td>0.194719</td>\n",
              "      <td>0.597360</td>\n",
              "      <td>96.991199</td>\n",
              "      <td>99.229319</td>\n",
              "      <td>0.594070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.400329</td>\n",
              "      <td>0.774994</td>\n",
              "      <td>1.970459</td>\n",
              "      <td>1.986812</td>\n",
              "      <td>0.983607</td>\n",
              "      <td>0.869357</td>\n",
              "      <td>0.991770</td>\n",
              "      <td>0.953647</td>\n",
              "      <td>0.198020</td>\n",
              "      <td>0.795380</td>\n",
              "      <td>97.045934</td>\n",
              "      <td>98.681226</td>\n",
              "      <td>0.788801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>0.500824</td>\n",
              "      <td>0.449722</td>\n",
              "      <td>1.280799</td>\n",
              "      <td>1.845145</td>\n",
              "      <td>0.639344</td>\n",
              "      <td>0.609772</td>\n",
              "      <td>0.921053</td>\n",
              "      <td>0.884646</td>\n",
              "      <td>0.128713</td>\n",
              "      <td>0.924092</td>\n",
              "      <td>28.079857</td>\n",
              "      <td>84.514504</td>\n",
              "      <td>0.845145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>0.599671</td>\n",
              "      <td>0.220562</td>\n",
              "      <td>0.634378</td>\n",
              "      <td>1.645568</td>\n",
              "      <td>0.316667</td>\n",
              "      <td>0.332833</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.793687</td>\n",
              "      <td>0.062706</td>\n",
              "      <td>0.986799</td>\n",
              "      <td>-36.562156</td>\n",
              "      <td>64.556813</td>\n",
              "      <td>0.772983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0.700165</td>\n",
              "      <td>0.097272</td>\n",
              "      <td>0.131364</td>\n",
              "      <td>1.428235</td>\n",
              "      <td>0.065574</td>\n",
              "      <td>0.148837</td>\n",
              "      <td>0.712941</td>\n",
              "      <td>0.701132</td>\n",
              "      <td>0.013201</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-86.863604</td>\n",
              "      <td>42.823529</td>\n",
              "      <td>0.598684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0.799012</td>\n",
              "      <td>0.037899</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.251546</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.060527</td>\n",
              "      <td>0.624742</td>\n",
              "      <td>0.621882</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>25.154639</td>\n",
              "      <td>0.401316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0.899506</td>\n",
              "      <td>0.011835</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.111722</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.025715</td>\n",
              "      <td>0.554945</td>\n",
              "      <td>0.555278</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>11.172161</td>\n",
              "      <td>0.200658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000469</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004397</td>\n",
              "      <td>0.499176</td>\n",
              "      <td>0.499917</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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          "text": [
            "\n",
            "\n",
            "ModelMetricsBinomialGLM: stackedensemble\n",
            "** Reported on validation data. **\n",
            "\n",
            "MSE: 0.07563334941320102\n",
            "RMSE: 0.2750151803322882\n",
            "LogLoss: 0.251286872739174\n",
            "Null degrees of freedom: 180\n",
            "Residual degrees of freedom: 176\n",
            "Null deviance: 250.97578384206275\n",
            "Residual deviance: 90.96584793158101\n",
            "AIC: 100.96584793158101\n",
            "AUC: 0.9610741561961074\n",
            "AUCPR: 0.972166745366916\n",
            "Gini: 0.9221483123922147\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.33317394055922517: \n"
          ]
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          "data": {
            "text/plain": [
              "             0      1   Error           Rate\n",
              "0      0  70.0   12.0  0.1463    (12.0/82.0)\n",
              "1      1   6.0   93.0  0.0606     (6.0/99.0)\n",
              "2  Total  76.0  105.0  0.0994   (18.0/181.0)"
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              "      <td>0.1463</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>93.0</td>\n",
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              "      <th>2</th>\n",
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              "      <td>76.0</td>\n",
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          "text": [
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n"
          ]
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        {
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          "data": {
            "text/plain": [
              "                         metric  threshold      value    idx\n",
              "0                        max f1   0.333174   0.911765  104.0\n",
              "1                        max f2   0.303652   0.932540  107.0\n",
              "2                  max f0point5   0.699180   0.942350   87.0\n",
              "3                  max accuracy   0.699180   0.906077   87.0\n",
              "4                 max precision   0.996387   1.000000    0.0\n",
              "5                    max recall   0.020956   1.000000  165.0\n",
              "6               max specificity   0.996387   1.000000    0.0\n",
              "7              max absolute_mcc   0.699180   0.818679   87.0\n",
              "8    max min_per_class_accuracy   0.495009   0.898990   96.0\n",
              "9   max mean_per_class_accuracy   0.699180   0.911000   87.0\n",
              "10                      max tns   0.996387  82.000000    0.0\n",
              "11                      max fns   0.996387  98.000000    0.0\n",
              "12                      max fps   0.000442  82.000000  180.0\n",
              "13                      max tps   0.020956  99.000000  165.0\n",
              "14                      max tnr   0.996387   1.000000    0.0\n",
              "15                      max fnr   0.996387   0.989899    0.0\n",
              "16                      max fpr   0.000442   1.000000  180.0\n",
              "17                      max tpr   0.020956   1.000000  165.0"
            ],
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              "      <th>idx</th>\n",
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              "      <th>0</th>\n",
              "      <td>max f1</td>\n",
              "      <td>0.333174</td>\n",
              "      <td>0.911765</td>\n",
              "      <td>104.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>max f2</td>\n",
              "      <td>0.303652</td>\n",
              "      <td>0.932540</td>\n",
              "      <td>107.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>max f0point5</td>\n",
              "      <td>0.699180</td>\n",
              "      <td>0.942350</td>\n",
              "      <td>87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>max accuracy</td>\n",
              "      <td>0.699180</td>\n",
              "      <td>0.906077</td>\n",
              "      <td>87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max precision</td>\n",
              "      <td>0.996387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>max recall</td>\n",
              "      <td>0.020956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>max specificity</td>\n",
              "      <td>0.996387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max absolute_mcc</td>\n",
              "      <td>0.699180</td>\n",
              "      <td>0.818679</td>\n",
              "      <td>87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>max min_per_class_accuracy</td>\n",
              "      <td>0.495009</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>96.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max mean_per_class_accuracy</td>\n",
              "      <td>0.699180</td>\n",
              "      <td>0.911000</td>\n",
              "      <td>87.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max tns</td>\n",
              "      <td>0.996387</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>max fns</td>\n",
              "      <td>0.996387</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>max fps</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>max tps</td>\n",
              "      <td>0.020956</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>max tnr</td>\n",
              "      <td>0.996387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>max fnr</td>\n",
              "      <td>0.996387</td>\n",
              "      <td>0.989899</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>max fpr</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>max tpr</td>\n",
              "      <td>0.020956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "\n",
            "Gains/Lift Table: Avg response rate: 54.70 %, avg score: 54.76 %\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group  cumulative_data_fraction  lower_threshold      lift  \\\n",
              "0       1                  0.011050         0.995593  1.828283   \n",
              "1       2                  0.022099         0.995363  1.828283   \n",
              "2       3                  0.033149         0.994917  1.828283   \n",
              "3       4                  0.044199         0.994553  1.828283   \n",
              "4       5                  0.055249         0.994377  1.828283   \n",
              "5       6                  0.104972         0.989339  1.828283   \n",
              "6       7                  0.154696         0.983918  1.828283   \n",
              "7       8                  0.204420         0.979861  1.828283   \n",
              "8       9                  0.303867         0.955610  1.828283   \n",
              "9      10                  0.403315         0.884420  1.726712   \n",
              "10     11                  0.502762         0.616936  1.320426   \n",
              "11     12                  0.602210         0.300700  0.914141   \n",
              "12     13                  0.701657         0.163993  0.203143   \n",
              "13     14                  0.801105         0.079412  0.203143   \n",
              "14     15                  0.900552         0.022105  0.000000   \n",
              "15     16                  1.000000         0.000442  0.101571   \n",
              "\n",
              "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
              "0          1.828283       1.000000  0.996004                  1.000000   \n",
              "1          1.828283       1.000000  0.995575                  1.000000   \n",
              "2          1.828283       1.000000  0.995145                  1.000000   \n",
              "3          1.828283       1.000000  0.994647                  1.000000   \n",
              "4          1.828283       1.000000  0.994381                  1.000000   \n",
              "5          1.828283       1.000000  0.991197                  1.000000   \n",
              "6          1.828283       1.000000  0.986183                  1.000000   \n",
              "7          1.828283       1.000000  0.981958                  1.000000   \n",
              "8          1.828283       1.000000  0.968698                  1.000000   \n",
              "9          1.803238       0.944444  0.920431                  0.986301   \n",
              "10         1.707737       0.722222  0.767417                  0.934066   \n",
              "11         1.576684       0.500000  0.437964                  0.862385   \n",
              "12         1.382009       0.111111  0.221631                  0.755906   \n",
              "13         1.235667       0.111111  0.108017                  0.675862   \n",
              "14         1.099213       0.000000  0.040928                  0.601227   \n",
              "15         1.000000       0.055556  0.009094                  0.546961   \n",
              "\n",
              "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
              "0           0.996004      0.020202                 0.020202   82.828283   \n",
              "1           0.995789      0.020202                 0.040404   82.828283   \n",
              "2           0.995575      0.020202                 0.060606   82.828283   \n",
              "3           0.995343      0.020202                 0.080808   82.828283   \n",
              "4           0.995150      0.020202                 0.101010   82.828283   \n",
              "5           0.993278      0.090909                 0.191919   82.828283   \n",
              "6           0.990997      0.090909                 0.282828   82.828283   \n",
              "7           0.988798      0.090909                 0.373737   82.828283   \n",
              "8           0.982220      0.181818                 0.555556   82.828283   \n",
              "9           0.966984      0.171717                 0.727273   72.671156   \n",
              "10          0.927510      0.131313                 0.858586   32.042649   \n",
              "11          0.846667      0.090909                 0.949495   -8.585859   \n",
              "12          0.758079      0.020202                 0.969697  -79.685746   \n",
              "13          0.677382      0.020202                 0.989899  -79.685746   \n",
              "14          0.607099      0.000000                 0.989899 -100.000000   \n",
              "15          0.547629      0.010101                 1.000000  -89.842873   \n",
              "\n",
              "    cumulative_gain  kolmogorov_smirnov  \n",
              "0         82.828283            0.020202  \n",
              "1         82.828283            0.040404  \n",
              "2         82.828283            0.060606  \n",
              "3         82.828283            0.080808  \n",
              "4         82.828283            0.101010  \n",
              "5         82.828283            0.191919  \n",
              "6         82.828283            0.282828  \n",
              "7         82.828283            0.373737  \n",
              "8         82.828283            0.555556  \n",
              "9         80.323786            0.715078  \n",
              "10        70.773671            0.785415  \n",
              "11        57.668427            0.766568  \n",
              "12        38.200907            0.591648  \n",
              "13        23.566701            0.416728  \n",
              "14         9.921299            0.197216  \n",
              "15         0.000000            0.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf72a11b-461b-4481-a2af-7004bfaef931\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>group</th>\n",
              "      <th>cumulative_data_fraction</th>\n",
              "      <th>lower_threshold</th>\n",
              "      <th>lift</th>\n",
              "      <th>cumulative_lift</th>\n",
              "      <th>response_rate</th>\n",
              "      <th>score</th>\n",
              "      <th>cumulative_response_rate</th>\n",
              "      <th>cumulative_score</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>cumulative_capture_rate</th>\n",
              "      <th>gain</th>\n",
              "      <th>cumulative_gain</th>\n",
              "      <th>kolmogorov_smirnov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.011050</td>\n",
              "      <td>0.995593</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996004</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996004</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.020202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.022099</td>\n",
              "      <td>0.995363</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995575</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995789</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.040404</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.040404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.033149</td>\n",
              "      <td>0.994917</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995575</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.060606</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.060606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.044199</td>\n",
              "      <td>0.994553</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995343</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.080808</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.080808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.055249</td>\n",
              "      <td>0.994377</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994381</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995150</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.101010</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.101010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.104972</td>\n",
              "      <td>0.989339</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991197</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993278</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.191919</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.191919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.154696</td>\n",
              "      <td>0.983918</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986183</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990997</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.282828</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.282828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.204420</td>\n",
              "      <td>0.979861</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981958</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.988798</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.373737</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.373737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.303867</td>\n",
              "      <td>0.955610</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.828283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.968698</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.982220</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>82.828283</td>\n",
              "      <td>0.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.403315</td>\n",
              "      <td>0.884420</td>\n",
              "      <td>1.726712</td>\n",
              "      <td>1.803238</td>\n",
              "      <td>0.944444</td>\n",
              "      <td>0.920431</td>\n",
              "      <td>0.986301</td>\n",
              "      <td>0.966984</td>\n",
              "      <td>0.171717</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>72.671156</td>\n",
              "      <td>80.323786</td>\n",
              "      <td>0.715078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>0.502762</td>\n",
              "      <td>0.616936</td>\n",
              "      <td>1.320426</td>\n",
              "      <td>1.707737</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.767417</td>\n",
              "      <td>0.934066</td>\n",
              "      <td>0.927510</td>\n",
              "      <td>0.131313</td>\n",
              "      <td>0.858586</td>\n",
              "      <td>32.042649</td>\n",
              "      <td>70.773671</td>\n",
              "      <td>0.785415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>0.602210</td>\n",
              "      <td>0.300700</td>\n",
              "      <td>0.914141</td>\n",
              "      <td>1.576684</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.437964</td>\n",
              "      <td>0.862385</td>\n",
              "      <td>0.846667</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.949495</td>\n",
              "      <td>-8.585859</td>\n",
              "      <td>57.668427</td>\n",
              "      <td>0.766568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0.701657</td>\n",
              "      <td>0.163993</td>\n",
              "      <td>0.203143</td>\n",
              "      <td>1.382009</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.221631</td>\n",
              "      <td>0.755906</td>\n",
              "      <td>0.758079</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.969697</td>\n",
              "      <td>-79.685746</td>\n",
              "      <td>38.200907</td>\n",
              "      <td>0.591648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0.801105</td>\n",
              "      <td>0.079412</td>\n",
              "      <td>0.203143</td>\n",
              "      <td>1.235667</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.108017</td>\n",
              "      <td>0.675862</td>\n",
              "      <td>0.677382</td>\n",
              "      <td>0.020202</td>\n",
              "      <td>0.989899</td>\n",
              "      <td>-79.685746</td>\n",
              "      <td>23.566701</td>\n",
              "      <td>0.416728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0.900552</td>\n",
              "      <td>0.022105</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.099213</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.040928</td>\n",
              "      <td>0.601227</td>\n",
              "      <td>0.607099</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.989899</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>9.921299</td>\n",
              "      <td>0.197216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>0.101571</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.009094</td>\n",
              "      <td>0.546961</td>\n",
              "      <td>0.547629</td>\n",
              "      <td>0.010101</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-89.842873</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "</table>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
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        {
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          "text": [
            "\n",
            "\n",
            "ModelMetricsBinomialGLM: stackedensemble\n",
            "** Reported on cross-validation data. **\n",
            "\n",
            "MSE: 0.09597350756561955\n",
            "RMSE: 0.3097959127645482\n",
            "LogLoss: 0.3152694657207473\n",
            "Null degrees of freedom: 606\n",
            "Residual degrees of freedom: 603\n",
            "Null deviance: 842.0204788102894\n",
            "Residual deviance: 382.73713138498715\n",
            "AIC: 390.73713138498715\n",
            "AUC: 0.9375977071391349\n",
            "AUCPR: 0.9407574397528503\n",
            "Gini: 0.8751954142782699\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.5428542224941326: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              0      1   Error           Rate\n",
              "0      0  281.0   23.0  0.0757   (23.0/304.0)\n",
              "1      1   48.0  255.0  0.1584   (48.0/303.0)\n",
              "2  Total  329.0  278.0   0.117   (71.0/607.0)"
            ],
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              "      <td>0</td>\n",
              "      <td>281.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0757</td>\n",
              "      <td>(23.0/304.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>48.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>0.1584</td>\n",
              "      <td>(48.0/303.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>329.0</td>\n",
              "      <td>278.0</td>\n",
              "      <td>0.117</td>\n",
              "      <td>(71.0/607.0)</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "text": [
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                         metric  threshold       value    idx\n",
              "0                        max f1   0.542854    0.877797  169.0\n",
              "1                        max f2   0.269999    0.899555  240.0\n",
              "2                  max f0point5   0.706938    0.903475  143.0\n",
              "3                  max accuracy   0.542854    0.883031  169.0\n",
              "4                 max precision   0.998016    1.000000    0.0\n",
              "5                    max recall   0.020233    1.000000  374.0\n",
              "6               max specificity   0.998016    1.000000    0.0\n",
              "7              max absolute_mcc   0.542854    0.768643  169.0\n",
              "8    max min_per_class_accuracy   0.433619    0.858086  186.0\n",
              "9   max mean_per_class_accuracy   0.542854    0.882963  169.0\n",
              "10                      max tns   0.998016  304.000000    0.0\n",
              "11                      max fns   0.998016  302.000000    0.0\n",
              "12                      max fps   0.000797  304.000000  399.0\n",
              "13                      max tps   0.020233  303.000000  374.0\n",
              "14                      max tnr   0.998016    1.000000    0.0\n",
              "15                      max fnr   0.998016    0.996700    0.0\n",
              "16                      max fpr   0.000797    1.000000  399.0\n",
              "17                      max tpr   0.020233    1.000000  374.0"
            ],
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              "      <th></th>\n",
              "      <th>metric</th>\n",
              "      <th>threshold</th>\n",
              "      <th>value</th>\n",
              "      <th>idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>max f1</td>\n",
              "      <td>0.542854</td>\n",
              "      <td>0.877797</td>\n",
              "      <td>169.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>max f2</td>\n",
              "      <td>0.269999</td>\n",
              "      <td>0.899555</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>max f0point5</td>\n",
              "      <td>0.706938</td>\n",
              "      <td>0.903475</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>max accuracy</td>\n",
              "      <td>0.542854</td>\n",
              "      <td>0.883031</td>\n",
              "      <td>169.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max precision</td>\n",
              "      <td>0.998016</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>max recall</td>\n",
              "      <td>0.020233</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>374.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>max specificity</td>\n",
              "      <td>0.998016</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max absolute_mcc</td>\n",
              "      <td>0.542854</td>\n",
              "      <td>0.768643</td>\n",
              "      <td>169.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>max min_per_class_accuracy</td>\n",
              "      <td>0.433619</td>\n",
              "      <td>0.858086</td>\n",
              "      <td>186.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max mean_per_class_accuracy</td>\n",
              "      <td>0.542854</td>\n",
              "      <td>0.882963</td>\n",
              "      <td>169.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>max tns</td>\n",
              "      <td>0.998016</td>\n",
              "      <td>304.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>max fns</td>\n",
              "      <td>0.998016</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>max fps</td>\n",
              "      <td>0.000797</td>\n",
              "      <td>304.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>max tps</td>\n",
              "      <td>0.020233</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>374.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>max tnr</td>\n",
              "      <td>0.998016</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>max fnr</td>\n",
              "      <td>0.998016</td>\n",
              "      <td>0.996700</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>max fpr</td>\n",
              "      <td>0.000797</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>max tpr</td>\n",
              "      <td>0.020233</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>374.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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          "text": [
            "\n",
            "Gains/Lift Table: Avg response rate: 49.92 %, avg score: 49.87 %\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    group  cumulative_data_fraction  lower_threshold      lift  \\\n",
              "0       1                  0.011532         0.996141  2.003300   \n",
              "1       2                  0.021417         0.995643  2.003300   \n",
              "2       3                  0.031301         0.995333  2.003300   \n",
              "3       4                  0.041186         0.994618  1.669417   \n",
              "4       5                  0.051071         0.993691  2.003300   \n",
              "5       6                  0.100494         0.989099  2.003300   \n",
              "6       7                  0.149918         0.979770  2.003300   \n",
              "7       8                  0.200988         0.965395  2.003300   \n",
              "8       9                  0.299835         0.894789  1.836359   \n",
              "9      10                  0.400329         0.721996  1.740572   \n",
              "10     11                  0.500824         0.418403  1.018071   \n",
              "11     12                  0.599671         0.258613  0.767932   \n",
              "12     13                  0.700165         0.148081  0.295569   \n",
              "13     14                  0.799012         0.067191  0.166942   \n",
              "14     15                  0.899506         0.020746  0.164205   \n",
              "15     16                  1.000000         0.000657  0.032841   \n",
              "\n",
              "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
              "0          2.003300       1.000000  0.997036                  1.000000   \n",
              "1          2.003300       1.000000  0.995782                  1.000000   \n",
              "2          2.003300       1.000000  0.995475                  1.000000   \n",
              "3          1.923168       0.833333  0.994909                  0.960000   \n",
              "4          1.938678       1.000000  0.994242                  0.967742   \n",
              "5          1.970459       1.000000  0.991820                  0.983607   \n",
              "6          1.981286       1.000000  0.984613                  0.989011   \n",
              "7          1.986880       1.000000  0.973286                  0.991803   \n",
              "8          1.937257       0.916667  0.933338                  0.967033   \n",
              "9          1.887884       0.868852  0.823577                  0.942387   \n",
              "10         1.713349       0.508197  0.564339                  0.855263   \n",
              "11         1.557511       0.383333  0.332952                  0.777473   \n",
              "12         1.376385       0.147541  0.204214                  0.687059   \n",
              "13         1.226763       0.083333  0.104448                  0.612371   \n",
              "14         1.108053       0.081967  0.042064                  0.553114   \n",
              "15         1.000000       0.016393  0.007066                  0.499176   \n",
              "\n",
              "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
              "0           0.997036      0.023102                 0.023102  100.330033   \n",
              "1           0.996457      0.019802                 0.042904  100.330033   \n",
              "2           0.996147      0.019802                 0.062706  100.330033   \n",
              "3           0.995850      0.016502                 0.079208   66.941694   \n",
              "4           0.995539      0.019802                 0.099010  100.330033   \n",
              "5           0.993710      0.099010                 0.198020  100.330033   \n",
              "6           0.990711      0.099010                 0.297030  100.330033   \n",
              "7           0.986283      0.102310                 0.399340  100.330033   \n",
              "8           0.968829      0.181518                 0.580858   83.635864   \n",
              "9           0.932367      0.174917                 0.755776   74.057242   \n",
              "10          0.858519      0.102310                 0.858086    1.807066   \n",
              "11          0.771887      0.075908                 0.933993  -23.206821   \n",
              "12          0.690409      0.029703                 0.963696  -70.443110   \n",
              "13          0.617919      0.016502                 0.980198  -83.305831   \n",
              "14          0.553584      0.016502                 0.996700  -83.579505   \n",
              "15          0.498662      0.003300                 1.000000  -96.715901   \n",
              "\n",
              "    cumulative_gain  kolmogorov_smirnov  \n",
              "0        100.330033            0.023102  \n",
              "1        100.330033            0.042904  \n",
              "2        100.330033            0.062706  \n",
              "3         92.316832            0.075918  \n",
              "4         93.867774            0.095720  \n",
              "5         97.045934            0.194730  \n",
              "6         98.128604            0.293740  \n",
              "7         98.687984            0.396050  \n",
              "8         93.725746            0.561121  \n",
              "9         88.788385            0.709723  \n",
              "10        71.334897            0.713349  \n",
              "11        55.751097            0.667546  \n",
              "12        37.638517            0.526196  \n",
              "13        22.676329            0.361777  \n",
              "14        10.805256            0.194068  \n",
              "15         0.000000            0.000000  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>group</th>\n",
              "      <th>cumulative_data_fraction</th>\n",
              "      <th>lower_threshold</th>\n",
              "      <th>lift</th>\n",
              "      <th>cumulative_lift</th>\n",
              "      <th>response_rate</th>\n",
              "      <th>score</th>\n",
              "      <th>cumulative_response_rate</th>\n",
              "      <th>cumulative_score</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>cumulative_capture_rate</th>\n",
              "      <th>gain</th>\n",
              "      <th>cumulative_gain</th>\n",
              "      <th>kolmogorov_smirnov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.011532</td>\n",
              "      <td>0.996141</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997036</td>\n",
              "      <td>0.023102</td>\n",
              "      <td>0.023102</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.023102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.021417</td>\n",
              "      <td>0.995643</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996457</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.042904</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.042904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.031301</td>\n",
              "      <td>0.995333</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995475</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996147</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.062706</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>0.062706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.041186</td>\n",
              "      <td>0.994618</td>\n",
              "      <td>1.669417</td>\n",
              "      <td>1.923168</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.994909</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.995850</td>\n",
              "      <td>0.016502</td>\n",
              "      <td>0.079208</td>\n",
              "      <td>66.941694</td>\n",
              "      <td>92.316832</td>\n",
              "      <td>0.075918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.051071</td>\n",
              "      <td>0.993691</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.938678</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994242</td>\n",
              "      <td>0.967742</td>\n",
              "      <td>0.995539</td>\n",
              "      <td>0.019802</td>\n",
              "      <td>0.099010</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>93.867774</td>\n",
              "      <td>0.095720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.100494</td>\n",
              "      <td>0.989099</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.970459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991820</td>\n",
              "      <td>0.983607</td>\n",
              "      <td>0.993710</td>\n",
              "      <td>0.099010</td>\n",
              "      <td>0.198020</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>97.045934</td>\n",
              "      <td>0.194730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.149918</td>\n",
              "      <td>0.979770</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.981286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.984613</td>\n",
              "      <td>0.989011</td>\n",
              "      <td>0.990711</td>\n",
              "      <td>0.099010</td>\n",
              "      <td>0.297030</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>98.128604</td>\n",
              "      <td>0.293740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.200988</td>\n",
              "      <td>0.965395</td>\n",
              "      <td>2.003300</td>\n",
              "      <td>1.986880</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.973286</td>\n",
              "      <td>0.991803</td>\n",
              "      <td>0.986283</td>\n",
              "      <td>0.102310</td>\n",
              "      <td>0.399340</td>\n",
              "      <td>100.330033</td>\n",
              "      <td>98.687984</td>\n",
              "      <td>0.396050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.299835</td>\n",
              "      <td>0.894789</td>\n",
              "      <td>1.836359</td>\n",
              "      <td>1.937257</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.933338</td>\n",
              "      <td>0.967033</td>\n",
              "      <td>0.968829</td>\n",
              "      <td>0.181518</td>\n",
              "      <td>0.580858</td>\n",
              "      <td>83.635864</td>\n",
              "      <td>93.725746</td>\n",
              "      <td>0.561121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.400329</td>\n",
              "      <td>0.721996</td>\n",
              "      <td>1.740572</td>\n",
              "      <td>1.887884</td>\n",
              "      <td>0.868852</td>\n",
              "      <td>0.823577</td>\n",
              "      <td>0.942387</td>\n",
              "      <td>0.932367</td>\n",
              "      <td>0.174917</td>\n",
              "      <td>0.755776</td>\n",
              "      <td>74.057242</td>\n",
              "      <td>88.788385</td>\n",
              "      <td>0.709723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>0.500824</td>\n",
              "      <td>0.418403</td>\n",
              "      <td>1.018071</td>\n",
              "      <td>1.713349</td>\n",
              "      <td>0.508197</td>\n",
              "      <td>0.564339</td>\n",
              "      <td>0.855263</td>\n",
              "      <td>0.858519</td>\n",
              "      <td>0.102310</td>\n",
              "      <td>0.858086</td>\n",
              "      <td>1.807066</td>\n",
              "      <td>71.334897</td>\n",
              "      <td>0.713349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>0.599671</td>\n",
              "      <td>0.258613</td>\n",
              "      <td>0.767932</td>\n",
              "      <td>1.557511</td>\n",
              "      <td>0.383333</td>\n",
              "      <td>0.332952</td>\n",
              "      <td>0.777473</td>\n",
              "      <td>0.771887</td>\n",
              "      <td>0.075908</td>\n",
              "      <td>0.933993</td>\n",
              "      <td>-23.206821</td>\n",
              "      <td>55.751097</td>\n",
              "      <td>0.667546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0.700165</td>\n",
              "      <td>0.148081</td>\n",
              "      <td>0.295569</td>\n",
              "      <td>1.376385</td>\n",
              "      <td>0.147541</td>\n",
              "      <td>0.204214</td>\n",
              "      <td>0.687059</td>\n",
              "      <td>0.690409</td>\n",
              "      <td>0.029703</td>\n",
              "      <td>0.963696</td>\n",
              "      <td>-70.443110</td>\n",
              "      <td>37.638517</td>\n",
              "      <td>0.526196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0.799012</td>\n",
              "      <td>0.067191</td>\n",
              "      <td>0.166942</td>\n",
              "      <td>1.226763</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.104448</td>\n",
              "      <td>0.612371</td>\n",
              "      <td>0.617919</td>\n",
              "      <td>0.016502</td>\n",
              "      <td>0.980198</td>\n",
              "      <td>-83.305831</td>\n",
              "      <td>22.676329</td>\n",
              "      <td>0.361777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0.899506</td>\n",
              "      <td>0.020746</td>\n",
              "      <td>0.164205</td>\n",
              "      <td>1.108053</td>\n",
              "      <td>0.081967</td>\n",
              "      <td>0.042064</td>\n",
              "      <td>0.553114</td>\n",
              "      <td>0.553584</td>\n",
              "      <td>0.016502</td>\n",
              "      <td>0.996700</td>\n",
              "      <td>-83.579505</td>\n",
              "      <td>10.805256</td>\n",
              "      <td>0.194068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000657</td>\n",
              "      <td>0.032841</td>\n",
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          "text": [
            "\n",
            "\n",
            "Cross-Validation Metrics Summary: \n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                   mean         sd  cv_1_valid  cv_2_valid  \\\n",
              "0                  accuracy    0.885597   0.016320    0.895161    0.909091   \n",
              "1                       auc    0.937954   0.017699    0.956454    0.944099   \n",
              "2                       err    0.114403   0.016320    0.104839    0.090909   \n",
              "3                 err_count   13.800000   1.303840   13.000000   12.000000   \n",
              "4                  f0point5    0.900575   0.017096    0.917603    0.912052   \n",
              "5                        f1    0.881955   0.014851    0.882883    0.903226   \n",
              "6                        f2    0.864835   0.030831    0.850694    0.894569   \n",
              "7            lift_top_group    2.002709   0.089382    2.101695    2.095238   \n",
              "8                   logloss    0.315381   0.037278    0.267255    0.316413   \n",
              "9       max_per_class_error    0.154922   0.034153    0.169492    0.111111   \n",
              "10                      mcc    0.774064   0.030464    0.793829    0.817932   \n",
              "11  mean_per_class_accuracy    0.885223   0.015020    0.892177    0.908213   \n",
              "12     mean_per_class_error    0.114777   0.015020    0.107823    0.091787   \n",
              "13                      mse    0.096251   0.013085    0.082016    0.087186   \n",
              "14            null_deviance  168.404100  10.178322  172.038020  183.131800   \n",
              "15                   pr_auc    0.940129   0.019708    0.958947    0.910856   \n",
              "16                precision    0.913923   0.029965    0.942308    0.918033   \n",
              "17                       r2    0.614422   0.052156    0.671165    0.650534   \n",
              "18                   recall    0.854169   0.042365    0.830508    0.888889   \n",
              "19        residual_deviance   76.459370   8.902291   66.279360   83.533150   \n",
              "\n",
              "    cv_3_valid  cv_4_valid  cv_5_valid  \n",
              "0     0.868421    0.877049    0.878261  \n",
              "1     0.913713    0.925806    0.949697  \n",
              "2     0.131579    0.122951    0.121739  \n",
              "3    15.000000   15.000000   14.000000  \n",
              "4     0.907336    0.889262    0.876623  \n",
              "5     0.862385    0.876033    0.885246  \n",
              "6     0.821678    0.863192    0.894040  \n",
              "7     1.932203    1.967742    1.916667  \n",
              "8     0.361011    0.340176    0.292047  \n",
              "9     0.203390    0.145161    0.145455  \n",
              "10    0.747264    0.755143    0.756151  \n",
              "11    0.871032    0.877419    0.877273  \n",
              "12    0.128968    0.122581    0.122727  \n",
              "13    0.113075    0.106419    0.092561  \n",
              "14  158.130420  169.157320  159.562910  \n",
              "15    0.931982    0.942248    0.956615  \n",
              "16    0.940000    0.898305    0.870968  \n",
              "17    0.547144    0.574211    0.629055  \n",
              "18    0.796610    0.854839    0.900000  \n",
              "19   82.310555   83.002975   67.170820  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>sd</th>\n",
              "      <th>cv_1_valid</th>\n",
              "      <th>cv_2_valid</th>\n",
              "      <th>cv_3_valid</th>\n",
              "      <th>cv_4_valid</th>\n",
              "      <th>cv_5_valid</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>accuracy</td>\n",
              "      <td>0.885597</td>\n",
              "      <td>0.016320</td>\n",
              "      <td>0.895161</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>0.877049</td>\n",
              "      <td>0.878261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>auc</td>\n",
              "      <td>0.937954</td>\n",
              "      <td>0.017699</td>\n",
              "      <td>0.956454</td>\n",
              "      <td>0.944099</td>\n",
              "      <td>0.913713</td>\n",
              "      <td>0.925806</td>\n",
              "      <td>0.949697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>err</td>\n",
              "      <td>0.114403</td>\n",
              "      <td>0.016320</td>\n",
              "      <td>0.104839</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.131579</td>\n",
              "      <td>0.122951</td>\n",
              "      <td>0.121739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>err_count</td>\n",
              "      <td>13.800000</td>\n",
              "      <td>1.303840</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>f0point5</td>\n",
              "      <td>0.900575</td>\n",
              "      <td>0.017096</td>\n",
              "      <td>0.917603</td>\n",
              "      <td>0.912052</td>\n",
              "      <td>0.907336</td>\n",
              "      <td>0.889262</td>\n",
              "      <td>0.876623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>f1</td>\n",
              "      <td>0.881955</td>\n",
              "      <td>0.014851</td>\n",
              "      <td>0.882883</td>\n",
              "      <td>0.903226</td>\n",
              "      <td>0.862385</td>\n",
              "      <td>0.876033</td>\n",
              "      <td>0.885246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>f2</td>\n",
              "      <td>0.864835</td>\n",
              "      <td>0.030831</td>\n",
              "      <td>0.850694</td>\n",
              "      <td>0.894569</td>\n",
              "      <td>0.821678</td>\n",
              "      <td>0.863192</td>\n",
              "      <td>0.894040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>lift_top_group</td>\n",
              "      <td>2.002709</td>\n",
              "      <td>0.089382</td>\n",
              "      <td>2.101695</td>\n",
              "      <td>2.095238</td>\n",
              "      <td>1.932203</td>\n",
              "      <td>1.967742</td>\n",
              "      <td>1.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>logloss</td>\n",
              "      <td>0.315381</td>\n",
              "      <td>0.037278</td>\n",
              "      <td>0.267255</td>\n",
              "      <td>0.316413</td>\n",
              "      <td>0.361011</td>\n",
              "      <td>0.340176</td>\n",
              "      <td>0.292047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>max_per_class_error</td>\n",
              "      <td>0.154922</td>\n",
              "      <td>0.034153</td>\n",
              "      <td>0.169492</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.203390</td>\n",
              "      <td>0.145161</td>\n",
              "      <td>0.145455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>mcc</td>\n",
              "      <td>0.774064</td>\n",
              "      <td>0.030464</td>\n",
              "      <td>0.793829</td>\n",
              "      <td>0.817932</td>\n",
              "      <td>0.747264</td>\n",
              "      <td>0.755143</td>\n",
              "      <td>0.756151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>mean_per_class_accuracy</td>\n",
              "      <td>0.885223</td>\n",
              "      <td>0.015020</td>\n",
              "      <td>0.892177</td>\n",
              "      <td>0.908213</td>\n",
              "      <td>0.871032</td>\n",
              "      <td>0.877419</td>\n",
              "      <td>0.877273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>mean_per_class_error</td>\n",
              "      <td>0.114777</td>\n",
              "      <td>0.015020</td>\n",
              "      <td>0.107823</td>\n",
              "      <td>0.091787</td>\n",
              "      <td>0.128968</td>\n",
              "      <td>0.122581</td>\n",
              "      <td>0.122727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>mse</td>\n",
              "      <td>0.096251</td>\n",
              "      <td>0.013085</td>\n",
              "      <td>0.082016</td>\n",
              "      <td>0.087186</td>\n",
              "      <td>0.113075</td>\n",
              "      <td>0.106419</td>\n",
              "      <td>0.092561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>null_deviance</td>\n",
              "      <td>168.404100</td>\n",
              "      <td>10.178322</td>\n",
              "      <td>172.038020</td>\n",
              "      <td>183.131800</td>\n",
              "      <td>158.130420</td>\n",
              "      <td>169.157320</td>\n",
              "      <td>159.562910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>pr_auc</td>\n",
              "      <td>0.940129</td>\n",
              "      <td>0.019708</td>\n",
              "      <td>0.958947</td>\n",
              "      <td>0.910856</td>\n",
              "      <td>0.931982</td>\n",
              "      <td>0.942248</td>\n",
              "      <td>0.956615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>precision</td>\n",
              "      <td>0.913923</td>\n",
              "      <td>0.029965</td>\n",
              "      <td>0.942308</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>0.940000</td>\n",
              "      <td>0.898305</td>\n",
              "      <td>0.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>r2</td>\n",
              "      <td>0.614422</td>\n",
              "      <td>0.052156</td>\n",
              "      <td>0.671165</td>\n",
              "      <td>0.650534</td>\n",
              "      <td>0.547144</td>\n",
              "      <td>0.574211</td>\n",
              "      <td>0.629055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>recall</td>\n",
              "      <td>0.854169</td>\n",
              "      <td>0.042365</td>\n",
              "      <td>0.830508</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.796610</td>\n",
              "      <td>0.854839</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>residual_deviance</td>\n",
              "      <td>76.459370</td>\n",
              "      <td>8.902291</td>\n",
              "      <td>66.279360</td>\n",
              "      <td>83.533150</td>\n",
              "      <td>82.310555</td>\n",
              "      <td>83.002975</td>\n",
              "      <td>67.170820</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d81376b7-cd91-4399-aa9b-d2391c746263 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d81376b7-cd91-4399-aa9b-d2391c746263');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "See the whole table with table.as_data_frame()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lb = aml.leaderboard"
      ],
      "metadata": {
        "id": "8qdjINZyw_Ii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "_AAxJkPaxX0k",
        "outputId": "ae7e0903-19ea-42bf-f16a-6032c0ee8635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                              </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356</td><td style=\"text-align: right;\">0.937598</td><td style=\"text-align: right;\"> 0.315269</td><td style=\"text-align: right;\">0.940757</td><td style=\"text-align: right;\">              0.117037</td><td style=\"text-align: right;\">0.309796</td><td style=\"text-align: right;\">0.0959735</td></tr>\n",
              "<tr><td>StackedEnsemble_AllModels_1_AutoML_2_20220511_61356   </td><td style=\"text-align: right;\">0.934905</td><td style=\"text-align: right;\"> 0.323695</td><td style=\"text-align: right;\">0.932648</td><td style=\"text-align: right;\">              0.120348</td><td style=\"text-align: right;\">0.312413</td><td style=\"text-align: right;\">0.0976021</td></tr>\n",
              "<tr><td>XGBoost_2_AutoML_2_20220511_61356                     </td><td style=\"text-align: right;\">0.93281 </td><td style=\"text-align: right;\"> 0.322668</td><td style=\"text-align: right;\">0.938299</td><td style=\"text-align: right;\">              0.122004</td><td style=\"text-align: right;\">0.313339</td><td style=\"text-align: right;\">0.0981811</td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_2_20220511_61356                     </td><td style=\"text-align: right;\">0.932392</td><td style=\"text-align: right;\"> 0.330866</td><td style=\"text-align: right;\">0.929846</td><td style=\"text-align: right;\">              0.130168</td><td style=\"text-align: right;\">0.319367</td><td style=\"text-align: right;\">0.101995 </td></tr>\n",
              "<tr><td>GBM_2_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.926839</td><td style=\"text-align: right;\"> 0.353181</td><td style=\"text-align: right;\">0.923751</td><td style=\"text-align: right;\">              0.141713</td><td style=\"text-align: right;\">0.331589</td><td style=\"text-align: right;\">0.109951 </td></tr>\n",
              "<tr><td>XRT_1_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.925743</td><td style=\"text-align: right;\"> 0.546718</td><td style=\"text-align: right;\">0.932139</td><td style=\"text-align: right;\">              0.154774</td><td style=\"text-align: right;\">0.331096</td><td style=\"text-align: right;\">0.109625 </td></tr>\n",
              "<tr><td>GBM_3_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.923935</td><td style=\"text-align: right;\"> 0.358691</td><td style=\"text-align: right;\">0.917018</td><td style=\"text-align: right;\">              0.143374</td><td style=\"text-align: right;\">0.334959</td><td style=\"text-align: right;\">0.112197 </td></tr>\n",
              "<tr><td>DRF_1_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.922535</td><td style=\"text-align: right;\"> 0.705418</td><td style=\"text-align: right;\">0.921029</td><td style=\"text-align: right;\">              0.146669</td><td style=\"text-align: right;\">0.333494</td><td style=\"text-align: right;\">0.111218 </td></tr>\n",
              "<tr><td>GBM_4_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.921954</td><td style=\"text-align: right;\"> 0.36403 </td><td style=\"text-align: right;\">0.911036</td><td style=\"text-align: right;\">              0.151582</td><td style=\"text-align: right;\">0.336908</td><td style=\"text-align: right;\">0.113507 </td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_2_20220511_61356                     </td><td style=\"text-align: right;\">0.919142</td><td style=\"text-align: right;\"> 0.365454</td><td style=\"text-align: right;\">0.928126</td><td style=\"text-align: right;\">              0.130227</td><td style=\"text-align: right;\">0.336754</td><td style=\"text-align: right;\">0.113403 </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = aml.leader.predict(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vf-MPp2hxl3_",
        "outputId": "120f2183-1365-4182-ea7e-24cd06c31715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exa = aml.leader.explain(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q7idQpOgxrs_",
        "outputId": "9175d7ff-b078-43ac-fa43-fa143509be3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Confusion Matrix"
            ],
            "text/html": [
              "<h1>Confusion Matrix</h1>"
            ],
            "text/markdown": "\n\n# Confusion Matrix"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> Confusion matrix shows a predicted class vs an actual class."
            ],
            "text/html": [
              "<blockquote>Confusion matrix shows a predicted class vs an actual class.</blockquote>"
            ],
            "text/markdown": "\n> Confusion matrix shows a predicted class vs an actual class."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "## StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356"
            ],
            "text/html": [
              "<h2>StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356</h2>"
            ],
            "text/markdown": "\n\n## StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.3938662902106022: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              0     1   Error           Rate\n",
              "0      0  104.0  10.0  0.0877   (10.0/114.0)\n",
              "1      1   10.0  88.0   0.102    (10.0/98.0)\n",
              "2  Total  114.0  98.0  0.0943   (20.0/212.0)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e03b35b0-283a-46ef-9694-4d658fbb4979\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0877</td>\n",
              "      <td>(10.0/114.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.102</td>\n",
              "      <td>(10.0/98.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>114.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0943</td>\n",
              "      <td>(20.0/212.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e03b35b0-283a-46ef-9694-4d658fbb4979')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e03b35b0-283a-46ef-9694-4d658fbb4979 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e03b35b0-283a-46ef-9694-4d658fbb4979');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Partial Dependence Plots"
            ],
            "text/html": [
              "<h1>Partial Dependence Plots</h1>"
            ],
            "text/markdown": "\n\n# Partial Dependence Plots"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
            ],
            "text/html": [
              "<blockquote>Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.</blockquote>"
            ],
            "text/markdown": "\n> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exa = aml.explain(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4LeNwceQyCp8",
        "outputId": "81f56cc8-4306-4e0b-8144-85eb0d1951b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Leaderboard"
            ],
            "text/html": [
              "<h1>Leaderboard</h1>"
            ],
            "text/markdown": "\n\n# Leaderboard"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
            ],
            "text/html": [
              "<blockquote>Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.</blockquote>"
            ],
            "text/markdown": "\n> Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>model_id                                              </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th><th style=\"text-align: right;\">  training_time_ms</th><th style=\"text-align: right;\">  predict_time_per_row_ms</th><th>algo           </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356</td><td style=\"text-align: right;\">0.937598</td><td style=\"text-align: right;\"> 0.315269</td><td style=\"text-align: right;\">0.940757</td><td style=\"text-align: right;\">              0.117037</td><td style=\"text-align: right;\">0.309796</td><td style=\"text-align: right;\">0.0959735</td><td style=\"text-align: right;\">              2267</td><td style=\"text-align: right;\">                 0.182354</td><td>StackedEnsemble</td></tr>\n",
              "<tr><td>StackedEnsemble_AllModels_1_AutoML_2_20220511_61356   </td><td style=\"text-align: right;\">0.934905</td><td style=\"text-align: right;\"> 0.323695</td><td style=\"text-align: right;\">0.932648</td><td style=\"text-align: right;\">              0.120348</td><td style=\"text-align: right;\">0.312413</td><td style=\"text-align: right;\">0.0976021</td><td style=\"text-align: right;\">              2240</td><td style=\"text-align: right;\">                 0.128296</td><td>StackedEnsemble</td></tr>\n",
              "<tr><td>XGBoost_2_AutoML_2_20220511_61356                     </td><td style=\"text-align: right;\">0.93281 </td><td style=\"text-align: right;\"> 0.322668</td><td style=\"text-align: right;\">0.938299</td><td style=\"text-align: right;\">              0.122004</td><td style=\"text-align: right;\">0.313339</td><td style=\"text-align: right;\">0.0981811</td><td style=\"text-align: right;\">               382</td><td style=\"text-align: right;\">                 0.05575 </td><td>XGBoost        </td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_2_20220511_61356                     </td><td style=\"text-align: right;\">0.932392</td><td style=\"text-align: right;\"> 0.330866</td><td style=\"text-align: right;\">0.929846</td><td style=\"text-align: right;\">              0.130168</td><td style=\"text-align: right;\">0.319367</td><td style=\"text-align: right;\">0.101995 </td><td style=\"text-align: right;\">               318</td><td style=\"text-align: right;\">                 0.041687</td><td>XGBoost        </td></tr>\n",
              "<tr><td>GBM_2_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.926839</td><td style=\"text-align: right;\"> 0.353181</td><td style=\"text-align: right;\">0.923751</td><td style=\"text-align: right;\">              0.141713</td><td style=\"text-align: right;\">0.331589</td><td style=\"text-align: right;\">0.109951 </td><td style=\"text-align: right;\">               391</td><td style=\"text-align: right;\">                 0.102989</td><td>GBM            </td></tr>\n",
              "<tr><td>XRT_1_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.925743</td><td style=\"text-align: right;\"> 0.546718</td><td style=\"text-align: right;\">0.932139</td><td style=\"text-align: right;\">              0.154774</td><td style=\"text-align: right;\">0.331096</td><td style=\"text-align: right;\">0.109625 </td><td style=\"text-align: right;\">               366</td><td style=\"text-align: right;\">                 0.085227</td><td>DRF            </td></tr>\n",
              "<tr><td>GBM_3_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.923935</td><td style=\"text-align: right;\"> 0.358691</td><td style=\"text-align: right;\">0.917018</td><td style=\"text-align: right;\">              0.143374</td><td style=\"text-align: right;\">0.334959</td><td style=\"text-align: right;\">0.112197 </td><td style=\"text-align: right;\">               310</td><td style=\"text-align: right;\">                 0.093803</td><td>GBM            </td></tr>\n",
              "<tr><td>DRF_1_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.922535</td><td style=\"text-align: right;\"> 0.705418</td><td style=\"text-align: right;\">0.921029</td><td style=\"text-align: right;\">              0.146669</td><td style=\"text-align: right;\">0.333494</td><td style=\"text-align: right;\">0.111218 </td><td style=\"text-align: right;\">               235</td><td style=\"text-align: right;\">                 0.037496</td><td>DRF            </td></tr>\n",
              "<tr><td>GBM_4_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.921954</td><td style=\"text-align: right;\"> 0.36403 </td><td style=\"text-align: right;\">0.911036</td><td style=\"text-align: right;\">              0.151582</td><td style=\"text-align: right;\">0.336908</td><td style=\"text-align: right;\">0.113507 </td><td style=\"text-align: right;\">               318</td><td style=\"text-align: right;\">                 0.058547</td><td>GBM            </td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_2_20220511_61356                     </td><td style=\"text-align: right;\">0.919142</td><td style=\"text-align: right;\"> 0.365454</td><td style=\"text-align: right;\">0.928126</td><td style=\"text-align: right;\">              0.130227</td><td style=\"text-align: right;\">0.336754</td><td style=\"text-align: right;\">0.113403 </td><td style=\"text-align: right;\">               488</td><td style=\"text-align: right;\">                 0.026868</td><td>XGBoost        </td></tr>\n",
              "<tr><td>GBM_1_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.844467</td><td style=\"text-align: right;\"> 0.456516</td><td style=\"text-align: right;\">0.859445</td><td style=\"text-align: right;\">              0.29291 </td><td style=\"text-align: right;\">0.387004</td><td style=\"text-align: right;\">0.149772 </td><td style=\"text-align: right;\">               376</td><td style=\"text-align: right;\">                 0.042758</td><td>GBM            </td></tr>\n",
              "<tr><td>GLM_1_AutoML_2_20220511_61356                         </td><td style=\"text-align: right;\">0.415087</td><td style=\"text-align: right;\"> 0.696821</td><td style=\"text-align: right;\">0.425961</td><td style=\"text-align: right;\">              0.486853</td><td style=\"text-align: right;\">0.501833</td><td style=\"text-align: right;\">0.251836 </td><td style=\"text-align: right;\">                41</td><td style=\"text-align: right;\">                 0.011641</td><td>GLM            </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Confusion Matrix"
            ],
            "text/html": [
              "<h1>Confusion Matrix</h1>"
            ],
            "text/markdown": "\n\n# Confusion Matrix"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> Confusion matrix shows a predicted class vs an actual class."
            ],
            "text/html": [
              "<blockquote>Confusion matrix shows a predicted class vs an actual class.</blockquote>"
            ],
            "text/markdown": "\n> Confusion matrix shows a predicted class vs an actual class."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "## StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356"
            ],
            "text/html": [
              "<h2>StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356</h2>"
            ],
            "text/markdown": "\n\n## StackedEnsemble_BestOfFamily_1_AutoML_2_20220511_61356"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.3938662902106022: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              0     1   Error           Rate\n",
              "0      0  104.0  10.0  0.0877   (10.0/114.0)\n",
              "1      1   10.0  88.0   0.102    (10.0/98.0)\n",
              "2  Total  114.0  98.0  0.0943   (20.0/212.0)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62f3376d-bac0-4e03-8497-d30b77a29794\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>Error</th>\n",
              "      <th>Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0877</td>\n",
              "      <td>(10.0/114.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.102</td>\n",
              "      <td>(10.0/98.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>114.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0943</td>\n",
              "      <td>(20.0/212.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62f3376d-bac0-4e03-8497-d30b77a29794')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-62f3376d-bac0-4e03-8497-d30b77a29794 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-62f3376d-bac0-4e03-8497-d30b77a29794');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Variable Importance"
            ],
            "text/html": [
              "<h1>Variable Importance</h1>"
            ],
            "text/markdown": "\n\n# Variable Importance"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> The variable importance plot shows the relative importance of the most important variables in the model."
            ],
            "text/html": [
              "<blockquote>The variable importance plot shows the relative importance of the most important variables in the model.</blockquote>"
            ],
            "text/markdown": "\n> The variable importance plot shows the relative importance of the most important variables in the model."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Variable Importance Heatmap"
            ],
            "text/html": [
              "<h1>Variable Importance Heatmap</h1>"
            ],
            "text/markdown": "\n\n# Variable Importance Heatmap"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
            ],
            "text/html": [
              "<blockquote>Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.</blockquote>"
            ],
            "text/markdown": "\n> Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Model Correlation"
            ],
            "text/html": [
              "<h1>Model Correlation</h1>"
            ],
            "text/markdown": "\n\n# Model Correlation"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
            ],
            "text/html": [
              "<blockquote>This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.</blockquote>"
            ],
            "text/markdown": "\n> This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# SHAP Summary"
            ],
            "text/html": [
              "<h1>SHAP Summary</h1>"
            ],
            "text/markdown": "\n\n# SHAP Summary"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
            ],
            "text/html": [
              "<blockquote>SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.</blockquote>"
            ],
            "text/markdown": "\n> SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "# Partial Dependence Plots"
            ],
            "text/html": [
              "<h1>Partial Dependence Plots</h1>"
            ],
            "text/markdown": "\n\n# Partial Dependence Plots"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
            ],
            "text/html": [
              "<blockquote>Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.</blockquote>"
            ],
            "text/markdown": "\n> Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NYZsnLQqsK3f"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}